A maioria das propostas de planejamento de rotas para robôs móveis não leva em conta a existência de soluções para problemas similares ao aprender a política para resolver um novo problema; e devido a isto, o problema de navegação corrente deve ser resolvido a partir do zero, o que pode ser excessivamente custoso em relação ao tempo. Neste trabalho é realizado o acoplamento do conhecimento prévio obtido de soluções similares, representado por uma política abstrata, a um processo de aprendizado por reforço. Além disto, este trabalho apresenta uma arquitetura para o aprendizado por reforço simultâneo, de nome ASAR, onde a política abstrata auxilia na inicialização da política para o problema concreto, e ambas as políticas são refinadas através da exploração. A fim de reduzir a perda de informação na construção da política abstrata é proposto um algoritmo, nomeado X-TILDE, que constrói uma política abstrata estocástica. A arquitetura proposta é comparada com um algoritmo de aprendizado padrão e os resultados demonstram que ela é eficaz em acelerar a construção da política para problemas práticos. / Most work in path-planning approaches for mobile robots does not take into account existing solutions to similar problems when learning a policy to solve a new problem, and consequently solves the current navigation problem from scratch, what can be very time consuming. In this work we couple a prior knowledge obtained from a similar solution to a reinforcement learning process. The prior knowledge is represented by an abstract policy. In addition, this work presents a framework for simultaneous reinforcement learning called ASAR, where the abstract policy helps start up the policy for the concrete problem, and both policies are refined through exploration. For the construction of the abstract policy we propose an algorithm called X-TILDE, that builds a stochastic abstract policy, in order to reduce the loss of information. The proposed framework is compared with a default learning algorithm and the results show that it is effective in speeding up policy construction for practical problems.

A maioria das propostas de planejamento de rotas para robôs móveis não leva em conta a existência de soluções para problemas similares ao aprender a política para resolver um novo problema; e devido a isto, o problema de navegação corrente deve ser resolvido a partir do zero, o que pode ser excessivamente custoso em relação ao tempo. Neste trabalho é realizado o acoplamento do conhecimento prévio obtido de soluções similares, representado por uma política abstrata, a um processo de aprendizado por reforço. Além disto, este trabalho apresenta uma arquitetura para o aprendizado por reforço simultâneo, de nome ASAR, onde a política abstrata auxilia na inicialização da política para o problema concreto, e ambas as políticas são refinadas através da exploração. A fim de reduzir a perda de informação na construção da política abstrata é proposto um algoritmo, nomeado X-TILDE, que constrói uma política abstrata estocástica. A arquitetura proposta é comparada com um algoritmo de aprendizado padrão e os resultados demonstram que ela é eficaz em acelerar a construção da política para problemas práticos. / Most work in path-planning approaches for mobile robots does not take into account existing solutions to similar problems when learning a policy to solve a new problem, and consequently solves the current navigation problem from scratch, what can be very time consuming. In this work we couple a prior knowledge obtained from a similar solution to a reinforcement learning process. The prior knowledge is represented by an abstract policy. In addition, this work presents a framework for simultaneous reinforcement learning called ASAR, where the abstract policy helps start up the policy for the concrete problem, and both policies are refined through exploration. For the construction of the abstract policy we propose an algorithm called X-TILDE, that builds a stochastic abstract policy, in order to reduce the loss of information. The proposed framework is compared with a default learning algorithm and the results show that it is effective in speeding up policy construction for practical problems.

Range-based localization is a widely used technique for position estimation where distances are measured to anchors, nodes with known positions, and the position is analytically estimated. It offers the benefits of providing high localization accuracy and involving simple operation over multiple deployments. Examples are the Global Positioning System (GPS) and network-based cellular handset localization. Range-based localization is promising for a range of applications, such as robot deployment in emergency scenarios or monitoring industrial processes. However, the presence of clutter in some of these environments leads to a severe degradation of the localization accuracy due to non-line-of-sight (NLOS) signal propagation. Moreover, current literature in NLOS-mitigation techniques requires that the NLOS distances constitute only a minority of the total number of distances to anchors. The key ideas proposed in the dissertation are: 1) multi-hop localization offers significant advantages over single-hop localization in NLOS-prone environments; and 2) it is possible to further reduce position errors by carefully placing intermediate nodes among the clutter to minimize multi-hop distances between the anchors and the unlocalized node. We demonstrate that shortest path distance (SPD) based multi-hop localization algorithms, namely DV-Distance and MDS-MAP, perform the best among other competing techniques in NLOS-prone settings. However, with random node placement, these algorithms require large node densities to produce high localization accuracy. To tackle this, we show that the strategic placement of a relatively small number of nodes in the clutter can offer significant benefits. We propose two algorithms for node placement: first, the Optimal Placement for DV-Distance (OPDV) focuses on obtaining the optimal positions of the nodes for a known clutter topology; and second, the Adaptive Placement for DV-Distance (APDV) offers a distributed control technique that carefully moves nodes in the monitored area to achieve localization accuracies close to those achieved by OPDV. We evaluate both algorithms via extensive simulations, as well as demonstrate the APDV algorithm on a real robotic hardware platform. We finally demonstrate how the characteristics of the clutter topology influence single-hop and multi-hop distance errors, which in turn, impact the performance of the proposed algorithms.

2D to 3D reconstruction is an ill-posed problem in the field of Autonomous Robot Navigation. Many practitioners are tend to utilize the enormous success of Deep Learning techniques like CNN, ANN etc to solve tasks related to this 2D to 3D reconstruction. Generally, every deep learning model involves implementation of different optimizers related to the tasks to lower the possible negativity in its results and selection of hyper parameter values for these optimizers during the process of training the model with required dataset.Selection of this optimizer hyper-parameters requires in-depth knowledge and trials and errors. So proposing optimal hyper parameters for optimizers results in no waste in computational resources and time.Hence solution for the selected task cab found easily. The main objective of this research is to propose optimal hyper parameter values of various deep learning optimizers related to 2D to 3D reconstruction and proposing best optimizer among them in terms of computational time and resources To achieve the goal of this study two research methods are used in our work. The first one is a Systematic Literature Review; whose main goal is to reveal the widely selected and used optimizers for 2D to 3D reconstruction model using 3D Deep Learning techniques.The second, an experimental methodology is deployed, whose main goal is to propose the optimal hyper parameter values for respective optimizers like Adam, SGD+Momentum, Adagrad, Adadelta and Adamax which are used in 3D reconstruction models. In case of the computational time, Adamax optimizer outperformed all other optimizers used with training time (1970min), testing time (3360 min), evaluation-1 (16 min) and evaluation-2 (14 min).In case of Average Point cloud points, Adamax outperformed all other optimizers used with Mean value of 28451.04.In case of pred->GT and GT->pred values , Adamax optimizer outperformed all other optimizers with mean values of 4.742 and 4.600 respectively. Point Cloud Images with respective dense cloud points are obtained as results of our experiment.From the above results,Adamax optimizer is proved to be best in terms of visualization of Point Cloud images with optimal hyper parameter values as below:Epochs : 1000    Learning Rate : 1e-2    Chunk size : 32    Batch size : 32.  In this study,'Adamax' optimizer with optimal hyper para meter values and better Point Cloud Image is proven to be the best optimizer that can be used in a 2D to 3D reconstruction related task that deals with Point Cloud images

On assiste aujourd'hui à l'importation des NTIC (Nouvelles Technologies de l'Information et de la Télécommunication) dans la robotique. L'union de ces technologies donnera naissance, dans les années à venir, à la robotique de service grand-public.Cet avenir, s'il se réalise, sera le fruit d'un travail de recherche, amont, dans de nombreux domaines : la mécatronique, les télécommunications, l'automatique, le traitement du signal et des images, l'intelligence artificielle ... Un des aspects particulièrement intéressant en robotique mobile est alors le problème de la localisation et de la cartographie simultanée. En effet, dans de nombreux cas, un robot mobile, pour accéder à une intelligence, doit nécessairement se localiser dans son environnement. La question est alors : quelle précision pouvons-nous espérer en terme de localisation? Et à quel coût?Dans ce contexte, un des objectifs de tous les laboratoires de recherche en robotique, objectif dont les résultats sont particulièrement attendus dans les milieux industriels, est un positionnement et une cartographie de l'environnement, qui soient à la fois précis, tous-lieux, intègre, bas-coût et temps-réel. Les capteurs de prédilection sont les capteurs peu onéreux tels qu'un GPS standard (de précision métrique), et un ensemble de capteurs embarquables en charge utile (comme les caméras-vidéo). Ce type de capteurs constituera donc notre support privilégié, dans notre travail de recherche. Dans cette thèse, nous aborderons le problème de la localisation d'un robot mobile, et nous choisirons de traiter notre problème par l'approche probabiliste. La démarche est la suivante, nous définissons nos 'variables d'intérêt' : un ensemble de variables aléatoires. Nous décrivons ensuite leurs lois de distribution, et leur modèles d'évolution, enfin nous déterminons une fonction de coût, de manière à construire un observateur (une classe d'algorithme dont l'objectif est de déterminer le minimum de notre fonction de coût). Notre contribution consistera en l'utilisation de mesures GPS brutes GPS (les mesures brutes - ou raw-datas - sont les mesures issues des boucles de corrélation de code et de phase, respectivement appelées mesures de pseudo-distances de code et de phase) pour une navigation bas-coût précise en milieu extérieur suburbain. En utilisant la propriété dite 'entière' des ambiguïtés de phase GPS, nous étendrons notre navigation pour réaliser un système GPS-RTK (Real Time Kinematic) en mode différentiel local précise et bas-coût. Nos propositions sont validées par des expérimentations réalisées sur notre démonstrateur robotique. / We are witnessing nowadays the importation of ICT (Information and Communications Technology) in robotics. These technologies will give birth, in upcoming years, to the general public service robotics. This future, if realised, shall be the result of many research conducted in several domains: mechatronics, telecommunications, automatics, signal and image processing, artificial intelligence ... One particularly interesting aspect in mobile robotics is hence the simultaneous localisation and mapping problem. Consequently, to access certain informations, a mobile robot has, in many cases, to map/localise itself inside its environment. The following question is then posed: What precision can we aim for in terms of localisation? And at what cost?In this context, one of the objectives of many laboratories indulged in robotics research, and where results impact directly the industry, is the positioning and mapping of the environment. These latter tasks should be precise, adapted everywhere, integrated, low-cost and real-time. The prediction sensors are inexpensive ones, such as a standard GPS (of metric precision), and a set of embeddable payload sensors (e.g. video cameras). These type of sensors constitute the main support in our work.In this thesis, we shed light on the localisation problem of a mobile robot, which we choose to handle with a probabilistic approach. The procedure is as follows: we first define our "variables of interest" which are a set of random variables, and then we describe their distribution laws and their evolution models. Afterwards, we determine a cost function in such a manner to build up an observer (an algorithmic class where the objective is to minimize the cost function).Our contribution consists of using brute GPS measures (brute measures or raw datas are measures issued from code and phase correlation loops, called pseudo-distance measures of code and phase, respectively) for a low-cost navigation, which is precise in an external suburban environment. By implementing the so-called "whole" property of GPS phase ambiguities, we expand the navigation to achieve a GPS-RTK (Real-Time Kinematic) system in a precise and low-cost local differential mode.Our propositions has been validated through experimentations realized on our robotic demonstrator.

Cette thèse explore les capacités d’une intelligence ambiante équipée d’un réseau de capteurs de pression au sol. Elle traite le problème de la perception d’un environnement au travers un réseau de capteurs de basse résolution. Les difficultés incluent l’interpretation des poids dispersés pour des objets avec multiples supports, l’ambiguïté de poids entre des objets, la variation du poids des personnes pendant les activités dynamiques, etc. Nous introduisons des nouvelles techniques, partiellement inspirées du domaine de la vision par l’ordinateur, pour la détection, le suivi et la reconnaissance des entités qui se trouvent sur le sol. Nous introduisons également des nouveaux modes d’interaction entre les environnements équipés de tels capteurs aux sols, et les robots qui évoluent dans ces environnements. Ceci permet l’interprétation non-intrusive des événements qui ont lieu dans des environnements dotés d’une intelligence ambiante, avec des applications dans l’assistance automatisée à domicile, l’aide aux personnes âgées, le diagnostic continu de la santé, la sécurité, et la navigation robotique / This thesis explores the capabilities of an ambient intelligence equipped with a load-sensing floor. It deals with the problem of perceiving the environment through a network of low-resolution sensors. Challenges include the interpretation of spread loads for objects with multiple points of support, weight ambiguities between objects, variation of persons’ weight during dynamic activities, etc. We introduce new techniques, partly inspired from the field of computer vision, for detecting, tracking and recognizing the entities located on the floor. We also introduce new modes of interaction between environments equipped with such floor sensors and robots evolving inside them. This enables non-intrusive interpretation of events happening inside environments with embedded ambient intelligence, with applications in assisted living, senile care, continuous health diagnosis, home security, and robotic navigation

Les endoprothèses sont devenues une solution thérapeutique de choix pour les anévrismes aortiques. Même si leur bénéfice à long terme face à la chirurgie ouverte reste incertain, elles permettent de réduire la mortalité et les complications postopératoires précoces. Elles sont toutefois soumises à un certain nombre de contraintes anatomiques. Il y a encore quinze ans, elles étaient contre-indiquées dans le traitement des anévrismes débutant au niveau ou au-dessus des artères rénales, à savoir les anévrismes juxta et suprarénaux (AJSR) et les anévrismes thoraco-abdominaux (ATA). Le développement d'endoprothèses fenêtrées et/ou branchées (EPFB) a permis de proposer une solution endovasculaire à ces patients. Il s'agit d'endoprothèses munies d'orifices ou de branches latérales destinées à maintenir la perfusion des artères rénales et viscérales. Plusieurs études rétrospectives ont rapporté des résultats encourageants à court et moyen terme. Il s'agit néanmoins de techniques complexes, exposées à la survenue de problèmes techniques au niveau des artères cibles (rénales et viscérales) et des voies d'accès fémorales. Les résultats des EPFB nous semblent améliorables par une optimisation de la sélection des patients, par des innovations techniques apportées aux dispositifs implantables, ainsi que l'amélioration des techniques d'imagerie et de navigation endovasculaire. En raisons des délais de fabrication (> 6 semaines), les patients nécessitant un traitement rapide (anévrismes douloureux ou volumineux) échappent par ailleurs aux EPFB manufacturées.Les objectifs de cette thèse étaient: (1) d'évaluer dans notre expérience la fréquence et la nature des problèmes techniques peropératoires, et d'analyser leur impact sur les suites cliniques immédiates; (2) d'évaluer la faisabilité et les performances sur modèle in vitro de la navigation endovasculaire utilisant un système de navigation électromagnétique (StealthStation®, Medtronic); (3) d'évaluer la faisabilité et la sureté de la navigation robotique au cours de la mise en place d'une EPFB; (4) d'analyser les résultats à court terme des endoprothèses avec fenêtres créées à façon par le chirurgien pour des patients porteurs d'anévrismes complexes nécessitant un traitement rapide.Au cours de ce travail, nous avons tout d'abord montré qu'en début d'expérience (113 premiers patients), les problèmes techniques peropératoires n'étaient pas rares (30 % des patients) et que ceux-ci aboutissaient dans plus de la moitié des cas à des complications postopératoires. Ces problèmes techniques sont certes en partie évitables par l'amélioration de la sélection des patients et l'expertise de l'équipe soignante, mais nous pensons que le développement de nouveaux outils d'imagerie et de navigation pourrait également jouer un rôle clé. C'est sur ce dernier point que nous avons accentué nos recherches. Bien qu'une amélioration des performances techniques de la navigation électromagnétique soit nécessaire, nous avons pu montrer qu'elle permettait de fournir des informations complémentaires à la fluoroscopie, notamment lors des étapes de déploiement de l'endoprothèse et de cathétérisme des artères cibles. Plus que concurrente de la fluoroscopie, elle pourrait être utilisée de façon combinée dans le but de diminuer l'irradiation par les rayons X et d'améliorer les performances de la navigation. Nous avons également pu montrer les bénéfices potentiels de la navigation robotique lors de la mise en place d'endoprothèses branchées et ses limites actuelles pour assister de façon efficace les procédures fenêtrées. Enfin, dans une série limitée (n=11) de patients porteurs d'ATA, nous avons montré que l'utilisation d'endoprothèses avec fenêtres créées à façon par le chirurgien était à même de fournir des résultats précoces comparables (mortalité hospitalière : 9%, ischémie médullaire : 9%) à ceux des EPFB, et constituait une solution à considérer pour les patients nécessitant un traitement rapide. / Aortic stent grafts have gained widespread acceptance for the treatment of aortic aneurysms. When compared with open repair, they have been shown to reduce significantly postoperative mortality and complication rates. However, these benefits remain uncertain in the long-term and aortic stent grafts are still limited by anatomic restrictions. Fifteen years ago, patients with complex aneurysms such as juxtarenal, suprarenal and thoracoabdominal aneurysms were not eligible for endovascular repair. With the development of fenestrated and branched stent grafts (FBSG), it is now possible to offer an endovascular solution to these patients. FBSG contains holes (fenestrations) or lateral branches. Their purpose is to allow the proximal margin of the device to sit higher than standard infrarenal devices and allow uninterrupted blood flow to renal and visceral vessels. Retrospective studies have shown encouraging short-term and mid-term results. However, fenestrated/ branched stent grafting remains a complex procedure, associated with significant risks of technical problems such as target vessel loss and difficulties with iliac or femoral accesses. Results of FBSG could be further improved by optimizing patient selection, technical innovation to optimize currently available devices, and developments of new imaging and navigation tools. In addition, due to manufacturing delays (6-10 weeks), FBSG are not applicable to high-risk patients who need rapid treatment (painful or large aneurysms).The objectives of this thesis were: (1) to evaluate the incidence and define the nature of unexpected intraoperative adverse events and analyse their impact on the postoperative outcome; (2) to evaluate in vitro the feasibility and the performances of electromagnetic navigation using a new device (StealthStation®, Medtronic); (3) to evaluate the feasibility and safety of robotic navigation for target vessel cannulation; (4) to analyse short-term results of physician modified stent grafts for patients with complex aortic aneurysms needing rapid treatment.During this work, we showed that intraoperative adverse events were not rare (30% of the first 113 patients) during our early experience. They were responsible for postoperative complications in more than 50 % of cases. Optimal patient selection and increased experience of the medical staff may reduce the incidence of technical problems. However, we believe the development of new imaging modalities and navigation tools could play a key role to limit their occurrence. Although further developments are mandatory, the use of electromagnetic navigation as a complementary imaging modality, as tested with the StealthStation®, might be beneficial in terms of radiation exposure, cannulation performances, and accuracy in the positioning of FBSG. We also showed that robotic cannulation of renal and visceral vessels during complex endovascular aortic procedures is feasible and safe. In our experience, it was more effective for branches than for fenestrations. Finally, we used physician modified stent grafts in a limited series of eleven patients with thoracoabdominal aortic aneurysms. With a 9% in-hospital mortality rate and a 9% rate of spinal cord ischemia, they provided similar results to those of manufactured FBSG, suggesting they might be an option to consider for high-risk patients needing rapid treatment.